{"cells":[{"cell_type":"markdown","metadata":{},"source":["# 时间需预测问题\n"," \n","### 作业流程\n","+ 先导入数据,并对数据进行转换\n","+ 绘制时间序列图(折线图)\n","+ 观察3年销售额时间序图,总结\n","+ 预测第四年 ~~(ARIMA)~~ (winter模型) \n","+ 使用的模块\n","    + pandas 表格处理\n","    + matplotlib 基础绘图\n","    + statsmodels 统计模块(自相关图,偏相关图等)\n","    + scipy.stats\n","\n","扩展阅读\n","https://cloud.tencent.com/developer/article/1646121\n","https://blog.csdn.net/sanjianjixiang/article/details/103939699"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","import statsmodels.api as sm\n","from statsmodels.graphics.tsaplots import plot_acf, plot_pacf  # 非常规库,如果没有请剔除\n","from statsmodels.tsa.stattools import adfuller\n","\n","# from statsmodels.tsa.arima.model import ARIMA  # ARIMA模型导入\n","from scipy import stats\n","\n","plt.rcParams[\"font.family\"] = \"Microsoft YaHei\""]},{"cell_type":"markdown","metadata":{},"source":["### 0.数据预处理\n","+ 导入数据\n","+ 行列转换,将数据从12行3列转为36行2列"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 36 entries, 0 to 35\nData columns (total 4 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   月份      36 non-null     object \n 1   销售额     36 non-null     float64\n 2   年       36 non-null     object \n 3   年-月     36 non-null     object \ndtypes: float64(1), object(3)\nmemory usage: 1.2+ KB\n"]},{"output_type":"display_data","data":{"text/plain":"0   月份    销售额    年     年-月\n0    1  242.0  第一年   第一年-1\n1    2  235.0  第一年   第一年-2\n2    3  232.0  第一年   第一年-3\n3    4  178.0  第一年   第一年-4\n4    5  184.0  第一年   第一年-5\n5    6  140.0  第一年   第一年-6\n6    7  145.0  第一年   第一年-7\n7    8  152.0  第一年   第一年-8\n8    9  110.0  第一年   第一年-9\n9   10  130.0  第一年  第一年-10\n10  11  152.0  第一年  第一年-11\n11  12  206.0  第一年  第一年-12\n12   1  263.0  第二年   第二年-1\n13   2  238.0  第二年   第二年-2\n14   3  247.0  第二年   第二年-3\n15   4  193.0  第二年   第二年-4\n16   5  193.0  第二年   第二年-5\n17   6  149.0  第二年   第二年-6\n18   7  157.0  第二年   第二年-7\n19   8  161.0  第二年   第二年-8\n20   9  122.0  第二年   第二年-9\n21  10  130.0  第二年  第二年-10\n22  11  167.0  第二年  第二年-11\n23  12  230.0  第二年  第二年-12\n24   1  282.0  第三年   第三年-1\n25   2  255.0  第三年   第三年-2\n26   3  265.0  第三年   第三年-3\n27   4  205.0  第三年   第三年-4\n28   5  210.0  第三年   第三年-5\n29   6  160.0  第三年   第三年-6\n30   7  166.0  第三年   第三年-7\n31   8  174.0  第三年   第三年-8\n32   9  126.0  第三年   第三年-9\n33  10  148.0  第三年  第三年-10\n34  11  173.0  第三年  第三年-11\n35  12  235.0  第三年  第三年-12","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>月份</th>\n      <th>销售额</th>\n      <th>年</th>\n      <th>年-月</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>242.0</td>\n      <td>第一年</td>\n      <td>第一年-1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>235.0</td>\n      <td>第一年</td>\n      <td>第一年-2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>232.0</td>\n      <td>第一年</td>\n      <td>第一年-3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>178.0</td>\n      <td>第一年</td>\n      <td>第一年-4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>184.0</td>\n      <td>第一年</td>\n      <td>第一年-5</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>140.0</td>\n      <td>第一年</td>\n      <td>第一年-6</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>145.0</td>\n      <td>第一年</td>\n      <td>第一年-7</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>152.0</td>\n      <td>第一年</td>\n      <td>第一年-8</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>110.0</td>\n      <td>第一年</td>\n      <td>第一年-9</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>130.0</td>\n      <td>第一年</td>\n      <td>第一年-10</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>152.0</td>\n      <td>第一年</td>\n      <td>第一年-11</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>206.0</td>\n      <td>第一年</td>\n      <td>第一年-12</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1</td>\n      <td>263.0</td>\n      <td>第二年</td>\n      <td>第二年-1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2</td>\n      <td>238.0</td>\n      <td>第二年</td>\n      <td>第二年-2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>3</td>\n      <td>247.0</td>\n      <td>第二年</td>\n      <td>第二年-3</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4</td>\n      <td>193.0</td>\n      <td>第二年</td>\n      <td>第二年-4</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>5</td>\n      <td>193.0</td>\n      <td>第二年</td>\n      <td>第二年-5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>6</td>\n      <td>149.0</td>\n      <td>第二年</td>\n      <td>第二年-6</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>7</td>\n      <td>157.0</td>\n      <td>第二年</td>\n      <td>第二年-7</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>8</td>\n      <td>161.0</td>\n      <td>第二年</td>\n      <td>第二年-8</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>9</td>\n      <td>122.0</td>\n      <td>第二年</td>\n      <td>第二年-9</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>10</td>\n      <td>130.0</td>\n      <td>第二年</td>\n      <td>第二年-10</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>11</td>\n      <td>167.0</td>\n      <td>第二年</td>\n      <td>第二年-11</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>12</td>\n      <td>230.0</td>\n      <td>第二年</td>\n      <td>第二年-12</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1</td>\n      <td>282.0</td>\n      <td>第三年</td>\n      <td>第三年-1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2</td>\n      <td>255.0</td>\n      <td>第三年</td>\n      <td>第三年-2</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>3</td>\n      <td>265.0</td>\n      <td>第三年</td>\n      <td>第三年-3</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>4</td>\n      <td>205.0</td>\n      <td>第三年</td>\n      <td>第三年-4</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>5</td>\n      <td>210.0</td>\n      <td>第三年</td>\n      <td>第三年-5</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>6</td>\n      <td>160.0</td>\n      <td>第三年</td>\n      <td>第三年-6</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>7</td>\n      <td>166.0</td>\n      <td>第三年</td>\n      <td>第三年-7</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>8</td>\n      <td>174.0</td>\n      <td>第三年</td>\n      <td>第三年-8</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>9</td>\n      <td>126.0</td>\n      <td>第三年</td>\n      <td>第三年-9</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>10</td>\n      <td>148.0</td>\n      <td>第三年</td>\n      <td>第三年-10</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>11</td>\n      <td>173.0</td>\n      <td>第三年</td>\n      <td>第三年-11</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>12</td>\n      <td>235.0</td>\n      <td>第三年</td>\n      <td>第三年-12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"source":["df = pd.read_excel(\"作业数据.xlsx\", sheet_name=1)\n","df.columns = df.iloc[0, :]\n","df = df.iloc[1:, :]\n","# 对行列进行转换\n","con = []\n","for y_name in df.columns[1:]:\n","    tdf = df.loc[:, [\"月份\", y_name]].rename(columns={y_name: \"销售额\"})\n","    tdf[\"年\"] = y_name\n","    tdf[\"年-月\"] = tdf[\"年\"] + \"-\" + tdf[\"月份\"].astype(str)\n","    tdf[\"销售额\"] = tdf[\"销售额\"].astype(float)\n","    con.append(tdf)\n","tdf = pd.concat(con)\n","tdf = tdf.reset_index().drop(columns=\"index\")\n","tdf.info()\n","display(tdf)"]},{"cell_type":"markdown","metadata":{},"source":["### 1.展示过去三年销售额时间序列图\n","+ 折线图绘制\n","绘制后发现,数据中存在较强的周其性,但是趋势不显著\n","+ 趋势获取,\n","先不对数据进行处理"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["# 直接计算趋势线\n","linear = np.polyfit(tdf.index, tdf[\"销售额\"], deg=1)\n","y_prodict = np.polyval(linear, tdf.index)\n",""]},{"cell_type":"markdown","metadata":{},"source":["依照模型发现,整体趋势成持平可以看到模型中截距为 $a_1=-0.01$\n","- 但是由于没有剔除异常波动,需要对其进行周期计算"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 187.84384384384384, '直接计算趋势y=-0.01x+187.84')"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":"<Figure size 864x360 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD 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5))\n","plt.plot(tdf.index, tdf[\"销售额\"], label=\"销售额\", marker=\"o\")\n","plt.plot(tdf.index, y_prodict, label=\"趋势线\", linestyle=\":\", c=\"r\")\n","plt.xticks(tdf.index, tdf[\"年-月\"].values, rotation=-90)\n","plt.text(\n","    tdf.index[0],\n","    y_prodict[0],\n","    s=\"直接计算趋势y={:.2f}x+{:.2f}\".format(linear[0], linear[1]),\n","    color=\"r\",\n",")\n",""]},{"cell_type":"markdown","metadata":{},"source":["### 2.观察过去三年销售额时间序列图，总结销售额变化特点和规律\n","- 由之前模型总结得出\n","    - 1. 数据存在一定的周期性,目测为12个月一周期\n","    - 2. 整体趋势成轻微下滑,但是由于存在一定的扰动,需要剔除\n","- 结论\n","    - 通过ACF得知,数据以12个单位为一个周期,并且成拖尾\n","    - 通过PACF得知,模型趋势性较弱\n","    - ADF检验通过,得知模型相对平稳无需差分\n","- ARIMA选择AR模型"]},{"cell_type":"markdown","metadata":{},"source":["#### 自相关系数(ACF)\n","- 不难看出,自相关系数(ACF)中呈现了拖尾,且具有较好的周期性\n","- 从第一个节点0点处向后,因存在2个余弦周期,(cos(pi*x)/2) 第一个落点为3\n","- 通过图像观察,第一个完整周期也出现在12附近\n","#### 偏自相关系数(PACF) 严格自相关系数\n","- 呈现截尾"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["# 另一个比较粗暴的解决方法\n","# _tdf = tdf.copy()\n","# for i in range(30):\n","#     _tdf[\"销售额:\" + tdf[\"年-月\"].iloc[i]] = _tdf[\"销售额\"].shift(i + 1).astype(float)\n","\n","# corrs = _tdf.corr()\n","# plt.figure(figsize=(12, 12))\n","# plt.imshow(corrs, cmap=plt.cm.RdBu_r)\n","\n","# self_corr = _tdf.corr()[\"销售额\"]\n","# plt.figure(figsize=(12, 5))\n","# plt.bar(self_corr.index, self_corr.values)\n","# plt.xticks(self_corr.index, rotation=-90)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"output_type":"stream","name":"stderr","text":["d:\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1434: RuntimeWarning: invalid value encountered in sqrt\n  return rho, np.sqrt(sigmasq)\n"]},{"output_type":"execute_result","data":{"text/plain":["<Figure size 864x720 with 2 Axes>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with 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1. 周期检查 acf 与 pacf\n","fig = plt.figure(figsize=(12, 10))\n","ax1 = fig.add_subplot(2, 1, 1)\n","plot_acf(tdf[\"销售额\"].values, ax=ax1)\n","\n","ax2 = fig.add_subplot(2, 1, 2)\n","plot_pacf(tdf[\"销售额\"].values, ax=ax2)\n",""]},{"cell_type":"markdown","metadata":{},"source":["#### CMA 和 差分检验\n","为了验证周期情况通过,通过CMA(中心移动平均发进行检验)和ADF进行检验\n","adf_api: https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html\n","+ adf 检验后,adf值"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"{'adf值': -2.6933339317827896,\n '置信度p': 0.23886079223772516,\n '所使用的之后数': 13,\n '观测值数量': 22,\n '临界值': {'1%': -4.441609162283997,\n  '5%': -3.632984327573253,\n  '10%': -3.2547885800150262},\n 'icbest': 139.2621669649888}"},"metadata":{}}],"source":["adf_res = adfuller(\n","    tdf[\"销售额\"],\n","    maxlag=13,\n","    regression=\"ct\",  # 常数+趋势\n",")\n","display(\n","    dict(\n","        zip([\"adf值\", \"置信度p\", \"所使用的之后数\", \"观测值数量\", \"临界值\", \"icbest\", \"resstore\"], adf_res)\n","    )\n",")\n",""]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["# CMA均值也与adf检验结果一致,发现已12为周期波动较小\n","CMA = tdf[\"销售额\"].rolling(12).mean().shift(-6).dropna()\n",""]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 169.9412179487178, '直接计算趋势y=1.07x+169.94')"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":"<Figure size 864x360 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"339.254687pt\" version=\"1.1\" viewBox=\"0 0 708.592188 339.254687\" width=\"708.592188pt\" xmlns=\"http://www.w3.org/2000/svg\" 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\n"},"metadata":{"needs_background":"light"}}],"source":["linear = np.polyfit(CMA.index, CMA.values, deg=1)\n","cma_prodict = np.polyval(linear, tdf.index)\n","\n","plt.figure(figsize=(12, 5))\n","plt.plot(tdf.index, tdf[\"销售额\"], label=\"销售额\", marker=\"o\")\n","plt.plot(tdf.index, cma_prodict, label=\"CMA\", linestyle=\":\", c=\"r\")\n","plt.xticks(tdf.index, tdf[\"年-月\"].values, rotation=-90)\n","\n","\n","plt.text(\n","    tdf.index[0],\n","    cma_prodict[0],\n","    s=\"直接计算趋势y={:.2f}x+{:.2f}\".format(linear[0], linear[1]),\n","    color=\"r\",\n",")"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                                 OLS Regression Results                                \n=======================================================================================\nDep. Variable:                      y   R-squared (uncentered):                   1.000\nModel:                            OLS   Adj. R-squared (uncentered):              1.000\nMethod:                 Least Squares   F-statistic:                          8.514e+05\nDate:                Wed, 16 Jun 2021   Prob (F-statistic):                    1.00e-56\nTime:                        23:00:24   Log-Likelihood:                         -26.262\nNo. Observations:                  25   AIC:                                      56.52\nDf Residuals:                      23   BIC:                                      58.96\nDf Model:                           2                                                  \nCovariance Type:            nonrobust                                                  \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst        169.9412      0.369    460.048      0.000     169.177     170.705\nx1             1.0670      0.020     53.339      0.000       1.026       1.108\n==============================================================================\nOmnibus:                        3.601   Durbin-Watson:                   0.489\nProb(Omnibus):                  0.165   Jarque-Bera (JB):                2.515\nSkew:                          -0.776   Prob(JB):                        0.284\nKurtosis:                       3.085   Cond. No.                         47.4\n==============================================================================\n\nNotes:\n[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"","text/html":"<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   1.000</td> \n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   1.000</td> \n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>8.514e+05</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Wed, 16 Jun 2021</td> <th>  Prob (F-statistic):</th>          <td>1.00e-56</td> \n</tr>\n<tr>\n  <th>Time:</th>                 <td>23:00:24</td>     <th>  Log-Likelihood:    </th>          <td> -26.262</td> \n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th>          <td>   56.52</td> \n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    23</td>      <th>  BIC:               </th>          <td>   58.96</td> \n</tr>\n<tr>\n  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>const</th> <td>  169.9412</td> <td>    0.369</td> <td>  460.048</td> <td> 0.000</td> <td>  169.177</td> <td>  170.705</td>\n</tr>\n<tr>\n  <th>x1</th>    <td>    1.0670</td> <td>    0.020</td> <td>   53.339</td> <td> 0.000</td> <td>    1.026</td> <td>    1.108</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 3.601</td> <th>  Durbin-Watson:     </th> <td>   0.489</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.165</td> <th>  Jarque-Bera (JB):  </th> <td>   2.515</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td>-0.776</td> <th>  Prob(JB):          </th> <td>   0.284</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.085</td> <th>  Cond. No.          </th> <td>    47.4</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."},"metadata":{}}],"source":["# 验证模型准确度\n","X = sm.add_constant(CMA.index.tolist())\n","ols_model = sm.OLS(CMA.values.tolist(), X, hasconst=False).fit()\n","display(ols_model.summary())"]},{"cell_type":"markdown","metadata":{},"source":["+ 可以发现,模型拟合效果极佳,$R^2$达到了1\n","+ F检验和T检验p值显著性均通过检验(在正态分布的情况下,Y'和Y方差一致,x值均值一致)两者均通过显著性检验,模型有效"]},{"cell_type":"markdown","metadata":{},"source":["### 3.预测第四年1-12月的销售额\n","通过之前检验"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["# ARIMA\n","# ar = ARIMA(tdf[\"销售额\"],order=(10,0,0)).fit()\n","# print(ar.summary())\n","# plt.plot(\n","# ar.forecast(12, alpha=0.05))\n",""]},{"cell_type":"markdown","metadata":{},"source":["+ 计算季节因子,通过 $ 销售额 / CMA $"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["tdf = pd.merge(\n","    tdf,\n","    CMA,\n","    how=\"left\",\n","    left_index=True,\n","    right_index=True,\n",")\n","tdf[\"secson_index\"] = tdf[\"销售额_x\"] / tdf[\"销售额_y\"]"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"    secson_index\n月份              \n1       1.439630\n2       1.296047\n3       1.341171\n4       1.038820\n5       1.046531\n6       0.797574\n7       0.824188\n8       0.850741\n9       0.625401\n10      0.697899\n11      0.849960\n12      1.156230","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>secson_index</th>\n    </tr>\n    <tr>\n      <th>月份</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.439630</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.296047</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.341171</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.038820</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.046531</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.797574</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.824188</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.850741</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.625401</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.697899</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0.849960</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1.156230</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"'季节指数均值0.9970160109897613'"},"metadata":{}}],"source":["secson_index = tdf[[\"月份\", \"secson_index\"]].groupby(by=[\"月份\"]).mean()  # 计算各季节指数\n","display(secson_index)\n","display(\"季节指数均值{}\".format(secson_index.secson_index.mean()))"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    month  secson_index       sells  year year-month\n","0       1      1.443939  245.384695     1    y:1-m:1\n","1       2      1.299926  222.298026     1    y:1-m:2\n","2       3      1.345185  231.472973     1    y:1-m:3\n","3       4      1.041929  180.401926     1    y:1-m:4\n","4       5      1.049664  182.861022     1    y:1-m:5\n","5       6      0.799961  140.214038     1    y:1-m:6\n","6       7      0.826654  145.774832     1    y:1-m:7\n","7       8      0.853287  151.381826     1    y:1-m:8\n","8       9      0.627272  111.953771     1    y:1-m:9\n","9      10      0.699987  125.678615     1   y:1-m:10\n","10     11      0.852504  153.971686     1   y:1-m:11\n","11     12      1.159691  210.690398     1   y:1-m:12\n","12      1      1.443939  263.872664     2    y:2-m:1\n","13      2      1.299926  238.942079     2    y:2-m:2\n","14      3      1.345185  248.696515     2    y:2-m:3\n","15      4      1.041929  193.742630     2    y:2-m:4\n","16      5      1.049664  196.300754     2    y:2-m:5\n","17      6      0.799961  150.456612     2    y:2-m:6\n","18      7      0.826654  156.359187     2    y:2-m:7\n","19      8      0.853287  162.307186     2    y:2-m:8\n","20      9      0.627272  119.985271     2    y:2-m:9\n","21     10      0.699987  134.641146     2   y:2-m:10\n","22     11      0.852504  164.887017     2   y:2-m:11\n","23     12      1.159691  225.538901     2   y:2-m:12\n","24      1      1.443939  282.360632     3    y:3-m:1\n","25      2      1.299926  255.586133     3    y:3-m:2\n","26      3      1.345185  265.920058     3    y:3-m:3\n","27      4      1.041929  207.083334     3    y:3-m:4\n","28      5      1.049664  209.740485     3    y:3-m:5\n","29      6      0.799961  160.699186     3    y:3-m:6\n","30      7      0.826654  166.943543     3    y:3-m:7\n","31      8      0.853287  173.232547     3    y:3-m:8\n","32      9      0.627272  128.016770     3    y:3-m:9\n","33     10      0.699987  143.603678     3   y:3-m:10\n","34     11      0.852504  175.802347     3   y:3-m:11\n","35     12      1.159691  240.387403     3   y:3-m:12\n","36      1      1.443939  300.848601     4    y:4-m:1\n","37      2      1.299926  272.230187     4    y:4-m:2\n","38      3      1.345185  283.143600     4    y:4-m:3\n","39      4      1.041929  220.424037     4    y:4-m:4\n","40      5      1.049664  223.180216     4    y:4-m:5\n","41      6      0.799961  170.941760     4    y:4-m:6\n","42      7      0.826654  177.527899     4    y:4-m:7\n","43      8      0.853287  184.157907     4    y:4-m:8\n","44      9      0.627272  136.048270     4    y:4-m:9\n","45     10      0.699987  152.566209     4   y:4-m:10\n","46     11      0.852504  186.717678     4   y:4-m:11\n","47     12      1.159691  255.235906     4   y:4-m:12"],"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>month</th>\n      <th>secson_index</th>\n      <th>sells</th>\n      <th>year</th>\n      <th>year-month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.443939</td>\n      <td>245.384695</td>\n      <td>1</td>\n      <td>y:1-m:1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1.299926</td>\n      <td>222.298026</td>\n      <td>1</td>\n      <td>y:1-m:2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1.345185</td>\n      <td>231.472973</td>\n      <td>1</td>\n      <td>y:1-m:3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1.041929</td>\n      <td>180.401926</td>\n      <td>1</td>\n      <td>y:1-m:4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1.049664</td>\n      <td>182.861022</td>\n      <td>1</td>\n      <td>y:1-m:5</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>0.799961</td>\n      <td>140.214038</td>\n      <td>1</td>\n      <td>y:1-m:6</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0.826654</td>\n      <td>145.774832</td>\n      <td>1</td>\n      <td>y:1-m:7</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>0.853287</td>\n      <td>151.381826</td>\n      <td>1</td>\n      <td>y:1-m:8</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>0.627272</td>\n      <td>111.953771</td>\n      <td>1</td>\n      <td>y:1-m:9</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>0.699987</td>\n      <td>125.678615</td>\n      <td>1</td>\n      <td>y:1-m:10</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>0.852504</td>\n      <td>153.971686</td>\n      <td>1</td>\n      <td>y:1-m:11</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>1.159691</td>\n      <td>210.690398</td>\n      <td>1</td>\n      <td>y:1-m:12</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1</td>\n      <td>1.443939</td>\n      <td>263.872664</td>\n      <td>2</td>\n      <td>y:2-m:1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2</td>\n      <td>1.299926</td>\n      <td>238.942079</td>\n      <td>2</td>\n      <td>y:2-m:2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>3</td>\n      <td>1.345185</td>\n      <td>248.696515</td>\n      <td>2</td>\n      <td>y:2-m:3</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4</td>\n      <td>1.041929</td>\n      <td>193.742630</td>\n      <td>2</td>\n      <td>y:2-m:4</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>5</td>\n      <td>1.049664</td>\n      <td>196.300754</td>\n      <td>2</td>\n      <td>y:2-m:5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>6</td>\n      <td>0.799961</td>\n      <td>150.456612</td>\n      <td>2</td>\n      <td>y:2-m:6</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>7</td>\n      <td>0.826654</td>\n      <td>156.359187</td>\n      <td>2</td>\n      <td>y:2-m:7</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>8</td>\n      <td>0.853287</td>\n      <td>162.307186</td>\n      <td>2</td>\n      <td>y:2-m:8</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>9</td>\n      <td>0.627272</td>\n      <td>119.985271</td>\n      <td>2</td>\n      <td>y:2-m:9</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>10</td>\n      <td>0.699987</td>\n      <td>134.641146</td>\n      <td>2</td>\n      <td>y:2-m:10</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>11</td>\n      <td>0.852504</td>\n      <td>164.887017</td>\n      <td>2</td>\n      <td>y:2-m:11</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>12</td>\n      <td>1.159691</td>\n      <td>225.538901</td>\n      <td>2</td>\n      <td>y:2-m:12</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1</td>\n      <td>1.443939</td>\n      <td>282.360632</td>\n      <td>3</td>\n      <td>y:3-m:1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2</td>\n      <td>1.299926</td>\n      <td>255.586133</td>\n      <td>3</td>\n      <td>y:3-m:2</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>3</td>\n      <td>1.345185</td>\n      <td>265.920058</td>\n      <td>3</td>\n      <td>y:3-m:3</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>4</td>\n      <td>1.041929</td>\n      <td>207.083334</td>\n      <td>3</td>\n      <td>y:3-m:4</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>5</td>\n      <td>1.049664</td>\n      <td>209.740485</td>\n      <td>3</td>\n      <td>y:3-m:5</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>6</td>\n      <td>0.799961</td>\n      <td>160.699186</td>\n      <td>3</td>\n      <td>y:3-m:6</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>7</td>\n      <td>0.826654</td>\n      <td>166.943543</td>\n      <td>3</td>\n      <td>y:3-m:7</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>8</td>\n      <td>0.853287</td>\n      <td>173.232547</td>\n      <td>3</td>\n      <td>y:3-m:8</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>9</td>\n      <td>0.627272</td>\n      <td>128.016770</td>\n      <td>3</td>\n      <td>y:3-m:9</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>10</td>\n      <td>0.699987</td>\n      <td>143.603678</td>\n      <td>3</td>\n      <td>y:3-m:10</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>11</td>\n      <td>0.852504</td>\n      <td>175.802347</td>\n      <td>3</td>\n      <td>y:3-m:11</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>12</td>\n      <td>1.159691</td>\n      <td>240.387403</td>\n      <td>3</td>\n      <td>y:3-m:12</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>1</td>\n      <td>1.443939</td>\n      <td>300.848601</td>\n      <td>4</td>\n      <td>y:4-m:1</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>2</td>\n      <td>1.299926</td>\n      <td>272.230187</td>\n      <td>4</td>\n      <td>y:4-m:2</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>3</td>\n      <td>1.345185</td>\n      <td>283.143600</td>\n      <td>4</td>\n      <td>y:4-m:3</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>4</td>\n      <td>1.041929</td>\n      <td>220.424037</td>\n      <td>4</td>\n      <td>y:4-m:4</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>5</td>\n      <td>1.049664</td>\n      <td>223.180216</td>\n      <td>4</td>\n      <td>y:4-m:5</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>6</td>\n      <td>0.799961</td>\n      <td>170.941760</td>\n      <td>4</td>\n      <td>y:4-m:6</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>7</td>\n      <td>0.826654</td>\n      <td>177.527899</td>\n      <td>4</td>\n      <td>y:4-m:7</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>8</td>\n      <td>0.853287</td>\n      <td>184.157907</td>\n      <td>4</td>\n      <td>y:4-m:8</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>9</td>\n      <td>0.627272</td>\n      <td>136.048270</td>\n      <td>4</td>\n      <td>y:4-m:9</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>10</td>\n      <td>0.699987</td>\n      <td>152.566209</td>\n      <td>4</td>\n      <td>y:4-m:10</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>11</td>\n      <td>0.852504</td>\n      <td>186.717678</td>\n      <td>4</td>\n      <td>y:4-m:11</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>12</td>\n      <td>1.159691</td>\n      <td>255.235906</td>\n      <td>4</td>\n      <td>y:4-m:12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":14}],"source":["# 季节指数校准\n","secson_index = secson_index / secson_index.secson_index.mean()\n","# 重新赋值\n","tdf = pd.merge(tdf, secson_index, how=\"left\", left_on=\"月份\", right_index=True)\n","# 进行预测\n","t = []\n","for n in range(4):\n","    t.extend([i + 1 for i in range(12)])\n","t\n","_df = pd.DataFrame(t, columns=[\"month\"])\n","_df = pd.merge(_df, secson_index, how=\"left\", left_on=\"month\", right_index=True)\n","_df[\"sells\"] = (_df.index * ols_model.params[1] + ols_model.params[0]) * _df[\n","    \"secson_index\"\n","]\n","\n","_df[\"year\"] = _df.index // 12 + 1\n","_df[\"year-month\"] = \"y:\" + _df[\"year\"].astype(str) + \"-m:\" + _df[\"month\"].astype(str)\n","_df"]},{"cell_type":"markdown","metadata":{},"source":["#### 结果假设下检验\n","+ 配对t检验: 双尾$ p=0.532552969007966 $ 远大于 $\\\\a = 0.05 $\n","    + 接受 $ H_0 $ 预测值与实际值之间平均值不存在显著差异\n","+ ANOVA(方差一致性检验):$ p=0.9758031118109965 $ 远大于 $ \\\\a = 0.05 $\n","    + $ H_0 $ 预测值与实际值不存在显著差异,二类错误,故接受原假设"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"Ttest_relResult(statistic=-0.6303608435333545, pvalue=0.532552969007966)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"F_onewayResult(statistic=0.0009265672628621509, pvalue=0.9758031118109965)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"'两组样本的平均误差为3.1959013538008336'"},"metadata":{}}],"source":["display(stats.ttest_rel(tdf[\"销售额_x\"], _df.loc[: tdf.shape[0] - 1, \"sells\"]))\n","display(stats.f_oneway(tdf[\"销售额_x\"], _df.loc[: tdf.shape[0] - 1, \"sells\"]))\n","display(\n","    \"两组样本的平均误差为{}\".format(\n","        (sum((tdf[\"销售额_x\"] - _df.loc[: tdf.shape[0] - 1, \"sells\"]) ** 2) / tdf.shape[0])\n","        ** (1 / 2)\n","    )\n",")\n",""]},{"cell_type":"markdown","metadata":{},"source":["+ 通过模型得出,\n","    + 整体收入依然成缓慢增长,每月增长率约为1.07万元\n","    + 初始截距为169.94"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x2522c156fa0>"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":"<Figure size 864x360 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"334.598438pt\" version=\"1.1\" viewBox=\"0 0 708.592187 334.598438\" width=\"708.592187pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" 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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize=(12, 5))\n","plt.plot(tdf.index, tdf[\"销售额_x\"], label=\"原销售额\", marker=\"o\")\n","plt.plot(_df.index, _df.sells, label=\"预测销售额\", linestyle=\":\", c=\"r\")\n","plt.xticks(_df.index, _df[\"year-month\"].values, rotation=-90)\n","plt.plot([36], [295], label=\"要求参考点\", marker=\"o\", c=\"g\")\n","plt.text(\n","    _df.index.tolist()[0],\n","    _df.sells[0] + 30,\n","    s=\"直接计算趋势y={:.2f}*x*secson_index+{:.2f}\".format(\n","        ols_model.params[1], ols_model.params[0]\n","    ),\n","    color=\"r\",\n",")\n","plt.legend()"]},{"cell_type":"markdown","metadata":{},"source":["### 假设第四年1月份销售额为295万元，你的预测误差为多少？"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["第四年第一个月,预测值与实际值的误差为5.8486万元\n"]}],"source":["print(\"第四年第一个月,预测值与实际值的误差为{:.5}万元\".format(abs(_df.loc[36, \"sells\"] - 295)))"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":3},"orig_nbformat":4}}